Gemma 4 27B appends unsolicited content-warning disclaimers to 26.5% of criminal-law translations, degrading faithfulness

Auto-published from arXiv:2606.23375 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.78, no flags)

Category

over-refusal

Model

Gemma 4 27B

Surface

API / on-premises deployment

Setup

TF-RefusalBench translation task using the same prompt templates as above (e.g., "Traduisez le passage suivant en français:" plus a Swiss Supreme Court criminal-law passage). No system prompt or short institutional system prompt. The benchmark scores responses on two axes: outright refusal and disclaimer appended to an otherwise-complete response. Paper does not quote a verbatim model output, but describes disclaimers as content warnings appended after the translated text.

Observed behavior

Gemma 4 27B almost never refused outright (0.2% refusal rate) but appended unsolicited content warnings or safety disclaimers to 8.2–26.5% of translation outputs. These disclaimers are not captured by standard over-refusal metrics (which only flag complete refusals) but materially degrade task faithfulness for downstream legal use.

Expected behavior

The model should produce a clean, faithful translation with no appended commentary. Legal translation workflows require verbatim fidelity; added disclaimers corrupt the output and may mislead downstream processing.

Reproducibility

medium

Threat model

Legal professionals and automated pipelines that consume LLM translation outputs without manual review may silently propagate model-inserted disclaimers into official documents or downstream NLP steps, corrupting records or triggering false positives in downstream classifiers. This failure mode is invisible to monitoring systems that only track refusal rates.

Novelty

Demonstrates that disclaimer injection — compliant-but-contaminated output — is a distinct, harder-to-detect failure mode from outright refusal, and that standard over-refusal metrics miss it entirely; introduces a two-axis evaluation framework to capture both.

Source

• arXiv: 2606.23375
• PDF: https://arxiv.org/pdf/2606.23375v2
• Categories: cs.CL, cs.AI
• Authors: Arthur Wuhrmann, Gaetan Stein, Daniel Brunner, Andrei Kucharavy

Triage notes (auto)

• paperType: benchmark
• estimatedCaseCount: 2
• triage reason: Introduces TF-RefusalBench, a 5,200-prompt multilingual benchmark derived from real Swiss Supreme Court rulings that documents over-refusal on legitimate criminal law content—a reproducible model-level failure. Over-refusal is an explicitly relevant alignment failure, and the paper measures its impact on task faithfulness and tests concrete mitigations.

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